PEC2 - Visualización de datos

Librerias utilizadas durante la práctica:

library(dplyr)
library(readr)
library(ggplot2)
library(knitr)
library(plotly)
library(tidyverse)

En primer lugar cargamos los datos que hemos generado a partir del preprocesado en preprocess.ipybn y los examinamos brevemente para comprobar que son correctos.

Enlace al dataset original: https://www.kaggle.com/datasets/jsphyg/weather-dataset-rattle-package

df <- read_csv("../data/weatherAUS_clean.csv", show_col_types = FALSE)

cat("Información general del dataset:\n")

## Información general del dataset:

glimpse(df)

## Rows: 48
## Columns: 11
## $ year            <dbl> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, …
## $ month           <chr> "Apr", "Aug", "Dec", "Feb", "Jan", "Jul", "Jun", "Mar"…
## $ avg_temp        <dbl> 19.19, 15.45, 22.02, 22.89, 23.62, 14.32, 14.14, 22.16…
## $ max_temp        <dbl> 28.70, 24.70, 36.00, 39.65, 45.80, 24.30, 22.20, 33.50…
## $ min_temp        <dbl> 10.90, 7.00, 12.70, 15.35, 16.80, 7.10, 7.60, 13.90, 9…
## $ avg_rainfall    <dbl> 5.73, 0.49, 1.00, 1.80, 1.42, 1.05, 9.72, 2.19, 3.11, …
## $ avg_windspeed   <dbl> 15.43, 18.27, 18.24, 17.67, 16.77, 15.53, 16.57, 18.58…
## $ avg_sunshine    <dbl> 5.58, 4.53, 13.49, 10.86, 10.63, 3.73, 1.91, 5.55, 5.1…
## $ avg_evaporation <dbl> 3.97, 4.47, 7.83, 7.14, 7.89, 2.72, 2.56, 6.39, 3.26, …
## $ month_num       <dbl> 4, 8, 12, 2, 1, 7, 6, 3, 5, 11, 10, 9, 4, 8, 12, 2, 1,…
## $ season          <chr> "Autumn", "Winter", "Summer", "Summer", "Summer", "Win…

Gráfico de líneas

Para el primer gráfico, el gráfico de líneas, he decidido representar la temperatura promedio mensual de cada año del dataset.

Fuente: https://plotly.com/r/line-charts/

month_levels <- c("Jan","Feb","Mar","Apr","May","Jun",
                  "Jul","Aug","Sep","Oct","Nov","Dec")

df$month <- factor(df$month, levels = month_levels, ordered = TRUE)

df <- df %>% arrange(year, month)

df$year <- as.numeric(df$year)

years <- sort(unique(df$year))
num_years <- length(years)
color_palette <- colorRampPalette(c("yellow", "red"))(num_years)

fig <- plot_ly(type = "scatter", mode = "lines+markers")

for (i in seq_along(years)) {
  year_i <- years[i]
  year_data <- df %>% filter(year == year_i)
  
  marker_color <- adjustcolor(color_palette[i], red.f = 0.8, green.f = 0.8, blue.f = 0.8)

  fig <- fig %>%
    add_trace(
      data = year_data,
      x = ~month,
      y = ~avg_temp,
      name = as.character(year_i),
      line = list(color = color_palette[i], width = 4),
      marker = list(size = 6, symbol = "circle", color = marker_color)
    )
}

fig <- fig %>%
  layout(
    title = "Monthly Average Temperatures by Year (Sydney)",
    xaxis = list(title = "Month"),
    yaxis = list(title = "Temperature (°C)"),
    legend = list(title = list(text = "Year"))
  )

fig

Gráfico de Nightingale

Gráfico de Nightingale original, en el que se comparan las causas de muerte de los soldados:

Para el segundo gráfico, el Nightingale-Rose, utilizaré los datos de horas de sol promedio de cada estación.

Fuente: http://andrewtrick.com/coffee_rose.html

df_seasonal <- df %>%
  group_by(year, season) %>%
  summarise(avg_sunshine = mean(avg_sunshine, na.rm = TRUE)) %>%
  ungroup()

## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.

df_seasonal$season <- factor(df_seasonal$season,
                             levels = c("Winter", "Autumn", "Spring", "Summer"),
                             ordered = TRUE)

# --- Diagrama de Nightingale (por año y estación) ---
p <- ggplot(df_seasonal, aes(x = as.factor(year), y = avg_sunshine, fill = season)) +
  geom_bar(stat = "identity", width = 1, color = "white", alpha = 0.9) +
  coord_polar(start = 0) +
  theme_minimal() +
  scale_fill_manual(values = colorRampPalette(c("#FFF59D", "#FFB300", "#F57C00", "#E65100"))(4)) +
  theme(
    axis.title = element_blank(),
    axis.text.y = element_blank(),
    axis.text.x = element_text(size = 9, face = "bold"),
    panel.grid = element_blank(),
    legend.position = "bottom"
  ) +
  labs(
    title = "Seasonal Nightingale Chart: Average Sunshine by Year",
    subtitle = "Each bar segment represents a season (Summer, Autumn, Winter, Spring)",
    fill = "Season"
  )

p

Gráfico Rose - Wind Rose

Ejemplo de gráfico de rosa de vientos:

Para el gráfico radial de barras, he escogido representar la lluvia media de cada mes, en mm.

Fuente: https://r-graph-gallery.com/297-circular-barplot-with-groups.html

month_levels <- c("Jan","Feb","Mar","Apr","May","Jun",
                  "Jul","Aug","Sep","Oct","Nov","Dec")

df$month <- factor(df$month, levels = month_levels, ordered = TRUE)

df <- df %>% arrange(year, month)

data <- df %>%
  rename(group = year, value = avg_rainfall) %>%
  arrange(group, month) %>% 
  mutate(individual = month)

empty_bar <- 3
to_add <- data.frame(matrix(NA, empty_bar * nlevels(as.factor(data$group)), ncol(data)))
colnames(to_add) <- colnames(data)
to_add$group <- rep(levels(as.factor(data$group)), each = empty_bar)
data <- rbind(data, to_add)
# data <- data %>% arrange(group, desc(value)) # En caso de ordenar por tamaño, ranking de meses
data <- data %>% arrange(group, month)
data$id <- seq(1, nrow(data))

label_data <- data
number_of_bar <- nrow(label_data)
angle <- 90 - 360 * (label_data$id - 0.5) / number_of_bar
label_data$hjust <- ifelse(angle < -90, 1, 0)
label_data$angle <- ifelse(angle < -90, angle + 180, angle)

base_data <- data %>%
  filter(!is.na(group)) %>%
  group_by(group) %>%
  summarize(start = min(id), end = max(id) - empty_bar) %>%
  rowwise() %>%
  mutate(title = mean(c(start, end)))

grid_values <- seq(0, max(data$value, na.rm = TRUE), length.out = 5)[-1] 
grid_data <- base_data
grid_data$end <- grid_data$end[c(nrow(grid_data), 1:(nrow(grid_data)-1))] + 1
grid_data$start <- grid_data$start - 1
grid_data <- grid_data[-1,]

# --- GRAFICAR ---
p <- ggplot(data, aes(x=as.factor(id), y=value, fill=group)) +       
  
  geom_bar(aes(x=as.factor(id), y=value, fill=group), stat="identity", alpha=0.8) +
  
  geom_segment(data=grid_data, aes(x = end, y = 0, xend = start, yend = 0), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_data, aes(x = end, y = 2.5, xend = start, yend = 2.5), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_data, aes(x = end, y = 5, xend = start, yend = 5), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_data, aes(x = end, y = 7.5, xend = start, yend = 7.5), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_data, aes(x = end, y = 10, xend = start, yend = 10), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  
  annotate("text", x = rep(max(data$id),5), y = c(0, 2.5, 5, 7.5, 10), label = c("0", "2.5", "5", "7.5", "10") , color="grey", size=3 , angle=0, fontface="bold", hjust=1) +
  
  geom_bar(aes(x=as.factor(id), y=value, fill=group), stat="identity", alpha=0.8) +
  ylim(-10,11) +
  theme_minimal() +
  theme(
    legend.position = "none",
    axis.text = element_blank(),
    axis.title = element_blank(),
    panel.grid = element_blank(),
    plot.margin = unit(c(1, 1, 1, 1), "cm")
  ) +
  scale_fill_manual(values = colorRampPalette(c("#cce5ff", "#003366"))(length(unique(data$group)))) +
  coord_polar() + 
  geom_text(data=label_data, aes(x=id, y=value+1, label=individual, hjust=hjust), color="black", fontface="bold",alpha=0.6, size=2.5, angle= label_data$angle, inherit.aes = FALSE ) +
  
  geom_segment(data=base_data, aes(x = start, y = -1, xend = end, yend = -1), colour = "black", alpha=0.8, size=0.6 , inherit.aes = FALSE )  +
  geom_text(data=base_data, aes(x = title, y = -3, label=group), hjust=c(1,1,0,0), colour = "black", alpha=0.8, size=4, fontface="bold", inherit.aes = FALSE) +
  labs(
    title = "Anual Polar Bar Chart: Average Monthly Rainfall by Year",
    subtitle = "in average monthly mm."
  )

p